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Dynamic error spectrum for estimation performance evaluation: a case study on interacting multiple model algorithm

IET SIGNAL PROCESSING 2014年第2期8卷 202-210页

作者： Mao, Yanhui Han, Chongzhao Duan, Zhansheng Xi An Jiao Tong Univ Sch Elect & Informat Engn Key Lab Intelligent Networks & Network Secur MOE Minist EducInst Integrated Automat Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ CIESR Xian 710049 Shaanxi Peoples R China

The commonly used root-mean-square error for estimation performance evaluation is easily dominated by large error terms. So many new alternative absolute metrics have been provided in X. R. Li's work. However, each of these metrics only reflects one narrow aspect of estimation performance, respectively. A comprehensive measure, error spectrum, was presented aggregating all these incomprehensive measures. However, when being applied to dynamic systems, this measure will have three dimensions over the total time span, which is not intuitive and difficult to be analysed. To overcome its drawbacks, a new metric, dynamic error spectrum (DES), is proposed in this study to extend the error spectrum measure to dynamic systems. Three forms under different application backgrounds are given, one of which is balanced taking into account both good and bad behaviour of an estimator and so can provide more impartial evaluation results. It can be applied to a variety of dynamic systems directly. Then the challenge in performance evaluation of the interacting multiple model (IMM) algorithm is considered, and the IMM algorithm is chosen as the testing case to illustrate the superiority of the DES metric. The simulation results validate its utility and effectiveness.

关键词： estimation theory mean square error methods dynamic error spectrum estimation performance multiple model algorithm root-mean-square error performance evaluation dynamic systems interacting multiple model algorithm IMM algorithm DES metric

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PARALLEL IMPLEMENTATION OF multiple model TRACKING algorithmS

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 1991年第2期2卷 242-252页

作者： AVERBUCH, A ITZIKOWITZ, S KAPON, T School of Mathematical Sciences Tel-Aviv University Tel-Aviv Israel

Multifilter algorithms are usually applied in solving the maneuvering target tracking problem. However, in real, applications, processing time may become impractical. Multiprocessors seem to offer the throughput needed for such applications. In this paper, we investigate the performances of Viterbi algorithm and the interacting multiple model alogorithm, applied to the tracking problem, when implemented on a general purpose shared-memory and shared-bus MIMD multiprocessor. The computational complexity as well as the speedup and efficiency are examined in detail. It is shown that the computational complexity of the parallel implementation of these algorithms is about the same in both memory space and processing time categories. Efficiency with P processors is about 1 - 1/P for small P and is expected to be relatively high also for large P, especially when many filters and large state and measurement vectors are considered.

关键词： EXTENDED KALMAN FILTER (EKF) INTERACTING multiple model (IMM) algorithm MIMD MULTIPROCESSOR multiple model algorithm PARALLEL PROCESSING VITERBI algorithm (VA)

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Adaptive Multi-model Fault Diagnosis of Dynamic Systems for Motion Tracking

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IEEE ACCESS 2024年 12卷 197540-197556页

作者： Daniels, Annalena Benciolini, Tommaso Wollherr, Dirk Leibold, Marion Tech Univ Munich Chair Automat Control Engn D-80333 Munich Germany

For most real-world systems, the exact description of possible faults is unknown, making these faults difficult to detect, and even more difficult to identify. The most promising way is to use multiple hypotheses for faults to find the best fitting fault model by comparing system measurements with the predictions of the multi-model algorithm. However, this may lead to the need for infinite hypotheses. We propose a novel multi-model approach that considers a small number of different models with a known macro-structure and unknown parameters, combining system identification with simultaneous fault diagnosis. The unknown parameters in the models are estimated using a maximum likelihood approach. The fitted models are then used in an interacting multiple model algorithm to determine the most likely model that best describes the system behavior at any moment in time. An overfitting problem emerging from short data sequences is discussed, and two solutions are introduced. First, a regularization term in the probability estimation is suggested to penalize frequent parameter changes that signal possible overfitting. Second, an algorithm with a shifted data set is presented. The effectiveness of the algorithms is demonstrated on a motion tracking problem where the different fault hypotheses represent the macro-behavior of a moving object, and the real system switches between different modes. In a comparison, the proposed algorithms are the only ones that reliably identify the defined faults. They can be easily adapted to other fault diagnosis problems.

关键词： Adaptation models Maximum likelihood estimation Computational modeling Vectors Fault detection Switches Dynamical systems Adaptive systems System identification Robot sensing systems Parameter estimation fault diagnosis multiple model algorithm maximum likelihood estimation interacting multiple model algorithm

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BTP Prediction of Sintering Process by Using multiple models

BTP Prediction of Sintering Process by Using Multiple Models

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26th Chinese Control and Decision Conference (CCDC)

作者： Wang, Jialin Li, Xiaoli Li, Yang Wang, Kang Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9781479937066

Burning Through Point (BTP) state is a very important parameter for sintering process. A lot of researches for the modeling of BTP have been made, but the precise model is not very easy to find, so the prediction based on modeling cannot be carried out effectively. This paper presents fuzzy neural network structure and multiple model algorithms which can process incomplete, ambiguous information and gives an effective model for sintering process. The simulation result is made to show the effectiveness of the proposed algorithm.

关键词： BTP Fuzzy Neural Network multiple model algorithm Sintering Process

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Fault detection and isolation for the Bluebird test bed aircraft

Fault detection and isolation for the Bluebird test bed airc...

引用

作者： Levesque, Mario J. L. Naval Postgraduate School

学位级别：硕士

A Fault Detection and isolation (FDI) algorithm design is presented using the multiple model algorithm technique for the Bluebird aircraft being developed at the Naval Postgraduate School. The requirement to maintain high performance in the dynamic system of the aircraft necessitates the use of FDI techniques to detect and isolate malfunctions in the sensors and actuators of the aircraft without using hardware redundancy. The solution presented makes use of analytical redundancy in a bank of Kalman filters. Statistical tests using Bayesian theory are applied on the filter's innovations to perform the task of detection and isolation. The algorithm was developed using MATLAB software from The Math Works. Inc. The work presented in this thesis is related only to the task of FDI. The remaining task of the monitoring system, reconfiguration and continued operation by the observed plant after a failure detection, will not be addressed.

关键词： Fault Detection and Isolation Failure detection FDI sensor failure actuator failure multiple model algorithm Kalman filter

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ON-LINE ESTIMATION OF UNMEASURED INPUTS FOR ANAEROBIC PROCESS DESCRIBED BY INTERPOLATED L.T.I. modelS.

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IFAC Proceedings Volumes 2002年第1期35卷 407-412页

作者： D. Theilliol C. Aubrun J-C Ponsart J. Harm

A method for unknown input estimation in nonlinear stochastic system is presented. A key problem in bioprocess systems is the absence, in some cases, of reliable on line measurements for real time monitoring applications. In this paper, a software sensor for an anaerobic digester is presented. Unmeasured components of the influent are estimated from available on line measurements. Based on a multiple model scheme, a bank of unknown input Kalman filters are discussed to estimate a probabilistic weighting state and unknown input of the process. The performances of the method are tested in simulation using a validated model of an anaerobic fixed bed pilot plant.

关键词： Interpolated L.T.I. models unknown inputs Kalman filter multiple model algorithm nonlinear systems biological process

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BTP Prediction of Sintering Process by Using multiple models

BTP Prediction of Sintering Process by Using Multiple Models

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第26届中国控制与决策会议

作者： Jialin Wang Xiaoli Li Yang Li Kang Wang School of Automation and Electrical Engineering University of Science and Technology Beijing International Studies Communication University of China(CUC)

ISBN: (纸本)9781479937097

Burning Through Point(BTP) state is a very important parameter for sintering process.A lot of researches for the modeling of BTP have been made,but the precise model is not very easy to find,so the prediction based on modeling cannot be carried out *** paper presents fuzzy neural network structure and multiple model algorithms which can process incomplete,ambiguous information and gives an effective model for sintering *** simulation result is made to show the effectiveness of the proposed algorithm.

关键词： BTP Fuzzy Neural Network multiple model algorithm Sintering Process

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